3/07/2020

Our project

We decided to do central Colombia, basically because it is where the capital is.

We built a model for the number of confirmed cases using all the others covariates (plus some we created) and we estimated the predictive accuracy of our selected model.

Loading the dataset

colombia_covid <- as.data.frame(read_csv("data/datasets_567855_1056808_Casos1.csv"))
colnames(colombia_covid)[5] <- "Atención"
colnames(colombia_covid)[8] <- "Tipo"
# slicing the main dataset
central.colombia.dep <- c("Bogotá D.C.", "Tolima", "Cundinamarca", "Meta", "Boyacá", "Quindío", "Cauca",
    "Valle del Cauca", "Risaralda", "Caldas", "Boyacá", "Antioquia", "Santander", "Casanare")
central.colombia.rows <- which(colombia_covid$`Departamento o Distrito` %in% central.colombia.dep)
colombia_covid <- colombia_covid[central.colombia.rows, ]

Our cities

We decided to consider as central Colombia the following cities:

  • Bogotà DC,
  • Boyacá,
  • Tolima,
  • Cundinamarca,
  • Meta,
  • Quindío,
  • Valle del Cauca,
  • Risaralda, Celdas,
  • Boyacá,
  • Antioquia,
  • Santander
  • Casanare

Map

Here we can see our selected cities. The color of the pins is related with the number of cases: if they are less than \(10\) the color is “green”, if they are less than \(100\) the color is “orange”, otherwise it is “red”.

central_map

Preprocessing

head(colombia_covid)
##   ID de caso Fecha de diagnóstico Ciudad de ubicación Departamento o Distrito
## 1          1           2020-03-06              Bogotá             Bogotá D.C.
## 2          2           2020-03-09                Buga         Valle del Cauca
## 3          3           2020-03-09            Medellín               Antioquia
## 4          4           2020-03-11            Medellín               Antioquia
## 5          5           2020-03-11            Medellín               Antioquia
## 6          6           2020-03-11              Itagüí               Antioquia
##     Atención Edad Sexo        Tipo País de procedencia Veces Grupo de edad
## 1 Recuperado   19    F   Importado              Italia     1         19-30
## 2 Recuperado   34    M   Importado              España     2         31-45
## 3 Recuperado   50    F   Importado              España     2         46-60
## 4 Recuperado   55    M Relacionado            Colombia     3         46-60
## 5 Recuperado   25    M Relacionado            Colombia     3         19-30
## 6       Casa   27    F Relacionado            Colombia     3         19-30
##   Continente de procedencia
## 1                    Europa
## 2                    Europa
## 3                    Europa
## 4                  Colombia
## 5                  Colombia
## 6                  Colombia

Exploring the dataset

Number of cases confirmed day by day

The frequentist approach

Other slide

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The Bayesian approach

Other slide

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